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   <div id="projectname">Parallel Gaussian Process Regression
   &#160;<span id="projectnumber">1.0.0</span>
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   <div id="projectbrief">The implementation of parallel Gaussian process (GP) regression is based on the following publication: Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan &amp; Patrick Jaillet. Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), Bellevue, WA, Jul 11-15, 2013.</div>
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&#160;

<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>bandwidth
: <a class="el" href="structt__command__demo.html#af9e6d60a117f951bb76e4d30b9a179a4">t_command_demo</a>
</li>
<li>blk_size
: <a class="el" href="structt__command__prep.html#a854d30f09e6966153b6aad01be20ba75">t_command_prep</a>
</li>
<li>blocks
: <a class="el" href="structt__command__demo.html#a26890a40c95b0bcf54cc69b11f802dda">t_command_demo</a>
</li>
</ul>


<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>dd
: <a class="el" href="classpgpr__domain.html#a15bec9baded2abd76ff7d2b58fd6eacf">pgpr_domain</a>
</li>
<li>dim
: <a class="el" href="classpgpr__cov.html#ab24ca5303c1a5e6865109a8e1ba32f0f">pgpr_cov</a>
</li>
<li>domain
: <a class="el" href="classpgpr__data.html#a5b77dc4896f54924681a133309dc5206">pgpr_data</a>
</li>
<li>ds
: <a class="el" href="classpgpr__domain.html#ae75100ee1d73c7b7967ffb3fc8cb27c6">pgpr_domain</a>
</li>
</ul>


<h3><a class="anchor" id="index_h"></a>- h -</h3><ul>
<li>hyperf
: <a class="el" href="structt__command__demo.html#a3b5a4c26250a768829e7e7e3622774dc">t_command_demo</a>
, <a class="el" href="structt__command__prep.html#adacbfccb9bc1373a0383aeb7e392f303">t_command_prep</a>
</li>
</ul>


<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
<li>in_dim
: <a class="el" href="classpgpr__parse.html#a24a21542be9a07d290aed66f90997031">pgpr_parse</a>
</li>
</ul>


<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
<li>lsc
: <a class="el" href="classpgpr__cov.html#a43428fdb9c6278e5e2be3a4c60967b7b">pgpr_cov</a>
</li>
</ul>


<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
<li>m_connect
: <a class="el" href="classpgpr__parse.html#a1e1b0be13a510fd547620a8971874407">pgpr_parse</a>
</li>
<li>m_sample
: <a class="el" href="classpgpr__data.html#a839a49ca4d5de7973463e6203a7e807f">pgpr_data</a>
</li>
<li>m_xyz
: <a class="el" href="classpgpr__parse.html#ad68cc51bfd6c7c8193039f51b641e01d">pgpr_parse</a>
</li>
<li>mach_num
: <a class="el" href="structt__command__prep.html#ae291868f188ef559477de77a0c09b779">t_command_prep</a>
</li>
<li>mode
: <a class="el" href="structt__command__demo.html#ac45fbb0d03acb8884ce904e82b82e5bc">t_command_demo</a>
, <a class="el" href="structt__command__prep.html#a157a7013d31723e9e83ee5edd790a9c2">t_command_prep</a>
</li>
<li>mu
: <a class="el" href="classpgpr__cov.html#ad57517c36c587894616f19bd02025747">pgpr_cov</a>
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</ul>


<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>nos
: <a class="el" href="classpgpr__cov.html#aca7d95563680510edbd84957b34da53c">pgpr_cov</a>
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</ul>


<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
<li>out_dim
: <a class="el" href="classpgpr__parse.html#ae1fab280bf9c33d950d41f3c962fe8f0">pgpr_parse</a>
</li>
<li>outf
: <a class="el" href="structt__command__demo.html#a5243a68b75cfdcd0787feb1d1a24cdd9">t_command_demo</a>
</li>
</ul>


<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
<li>param_demo
: <a class="el" href="classpgpr__parse.html#ace1bdb63deaf2a5c680613c33cfc625e">pgpr_parse</a>
</li>
<li>param_prep
: <a class="el" href="classpgpr__parse.html#a86c109ef49507a1be375201d0ac4af8e">pgpr_parse</a>
</li>
<li>percent
: <a class="el" href="structt__command__prep.html#a4b948a1a01c4434772d8b34f91023a3b">t_command_prep</a>
</li>
</ul>


<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>rank
: <a class="el" href="structt__command__demo.html#a287322538dd7b8f950eeedba17a9a590">t_command_demo</a>
</li>
</ul>


<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
<li>seed
: <a class="el" href="structt__command__prep.html#a5ea7d62fa1431439c77ff1be76a2803b">t_command_prep</a>
</li>
<li>sig
: <a class="el" href="classpgpr__cov.html#a57b97be1a56d01ba78bb58200cc66244">pgpr_cov</a>
</li>
<li>ss
: <a class="el" href="structt__state.html#a642a193cb8c429653c7be9805b37cbf3">t_state</a>
</li>
<li>support
: <a class="el" href="structt__command__prep.html#ac4a2e1b1ae951cef4e6a1b75e5e5556d">t_command_prep</a>
</li>
<li>supportf
: <a class="el" href="structt__command__demo.html#a2253b0a9e63110ee5b1839122307e678">t_command_demo</a>
, <a class="el" href="structt__command__prep.html#a19ce353138f10d386cbd54bb9f4edb02">t_command_prep</a>
</li>
</ul>


<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
<li>testf
: <a class="el" href="structt__command__demo.html#a84873e5db588a94bca1f35551bf034e4">t_command_demo</a>
, <a class="el" href="structt__command__prep.html#af2e121153fa801d803b0df9b6a4e6180">t_command_prep</a>
</li>
<li>trainf
: <a class="el" href="structt__command__demo.html#a4caa14253026ed57aacca438cd672bb0">t_command_demo</a>
, <a class="el" href="structt__command__prep.html#a7c6fcd0ab10f8fb7d1f37209ff4754b3">t_command_prep</a>
</li>
</ul>


<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
<li>v_hyp
: <a class="el" href="classpgpr__parse.html#afca0013fbaf5205abef333d22f0bcdf4">pgpr_parse</a>
</li>
<li>v_param
: <a class="el" href="classpgpr__parse.html#aa2dc5cb24f1a14e11db04e129c070697">pgpr_parse</a>
</li>
<li>v_st
: <a class="el" href="structt__state.html#ab8fb1cce21d366d18a4b6c40161286bc">t_state</a>
</li>
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